lazymac-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It lists functional capabilities but doesn't describe how the tool behaves—e.g., whether it processes text synchronously or asynchronously, any rate limits, authentication needs, output format, or error handling. This leaves significant gaps in understanding the tool's operational traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise—just four terms separated by commas—and front-loaded with the key features. It wastes no words, but it's arguably too terse, lacking a complete sentence or structure that could better convey purpose. However, every term earns its place by indicating capabilities, so it scores well for efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (multiple analysis functions), no annotations, no output schema, and a nested input schema with free-form parameters, the description is incomplete. It doesn't explain how to use the tool effectively, what results to expect, or handle the ambiguity of the 'params' object. For a tool with such open-ended inputs and no structured outputs, more guidance is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, documenting a single 'params' object as free-form parameters passed in queries or JSON. The description adds no parameter-specific details beyond the schema, such as examples of common params (e.g., 'text' field) or how to structure them for different analyses. With high schema coverage, the baseline is 3, but the description doesn't compensate with additional semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description lists four analysis functions (sentiment, readability, keyword extraction, language detection), which gives a general idea of what the tool does, but it's vague about the specific action (e.g., 'analyzes text for' or 'performs text analysis on') and doesn't clearly distinguish it from sibling tools like 'seo_analyzer' or 'prompt_optimizer' that might overlap in text processing. It states what features are available but not the core verb+resource combination explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, and with many sibling tools (e.g., 'seo_analyzer', 'prompt_optimizer') that could handle similar text tasks, there's no indication of how this tool differs or when it's the preferred choice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.